Development of an IoT-Based Precision Irrigation System for Tomato Production from Indoor Seedling Germination to Outdoor Field Production
Source abstract
Abstract
Proper irrigation management, especially for tomatoes that are sensitive to water, is the key to ensuring sustainable tomato production. Using a low-cost sensor coupled with IoT technology could help to achieve precise control of the moisture content in the plant root-zone soil and apply water on demand with minimum human intervention. An IoT-based precision irrigation system was developed for growing Momotaro tomato seedlings inside a dark chamber. Four irrigation thresholds, 5%, 8%, 12%, and 15%, and two irrigation systems, surface and subsurface drip irrigation, were compared to assess which threshold and irrigation system referred the ideal tomato seedling growth. As a result, the 12% soil moisture threshold applied through the subsurface drip irrigation system significantly (p < 0.05) increased tomato seedling growth in soil composed of a main blend of peat moss, vermiculite, and perlite. Furthermore, in two repeated experiments, a subsurface drip irrigation system with 0.86 distribution uniformity used 10% less water than the surface drip irrigation system. The produced tomato seedlings were transplanted to open fields for further assessment. A low-power wide area networking Long Range Wide Area Network (LoRaWAN) protocol was developed with remote monitoring and controlling capability for irrigation management. Two irrigation systems, including surface and subsurface drip irrigations, were used to compare which system resulted in higher tomato yields. The results showed that the subsurface drip irrigation system with 0.74 distribution uniformity produced 1243 g/plant, while each plant produced 1061 g in the surface drip irrigation system treatment. The results also indicated that the LoRaWAN-based subsurface drip irrigation system was suitable under outdoor conditions with easy operation and robust controlling capability for tomato production.
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Research context
OpenAlex topics: Irrigation Practices and Water Management · Smart Agriculture and AI · Innovations in Aquaponics and Hydroponics Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Graduate School of Science and Technology, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8572, Ibaraki, Japan
Department of Soil Science and Irrigation, Faculty of Agriculture, Kabul University, Kabul 2496300, Afghanistan
Faculty of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8572, Ibaraki, Japan
Funding
This research did not receive any funding
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